The objective of see-and-avoid or sense-and-avoid (SAA) is to provide an unmanned aircraft system (UAS) with the capability to perform self-separation and collision avoidance against all air traffic, with or without active, transponder based collision avoidance systems. SAA requires the UAS to detect and track intruder aircraft in the operating vicinity of the ownship aircraft to identify guidance maneuvers required to perform self-separation and collision avoidance. The detect and track functions are key enablers for UAS SAA capability because the UAS cannot perform self-separation or collision avoidance maneuvers for undetected, untracked intruder aircraft. The detect function refers to using surveillance sensors to measure the position of intruder aircraft relative to the ownship UAS. The track function refers to fusing the surveillance sensor measurements together to estimate the trajectory statistics (also referred to herein as the track) of the intruder aircraft relative to the ownship UAS. The surveillance sensors provide measurements with corresponding measurement IDs that can be correlated across time or random across time.
The track function estimates the tracks of the intruder aircraft using a data association algorithm to assign measurements to a current track, a filter to fuse sensor measurements with the current estimates of the track statistics, and a trajectory manager that oversees the sensor fusion operation, initiates tracks, maintains tracks, and deletes tracks.
One tracking system uses random finite sets (RFS) to track multiple intruder aircraft (IA) operating in the environment of the ownship UAS. RFS are part of a multi-hypothesis testing (MHT) approach to multiple IA tracking RFS casts the multiple IA tracking problem into a set-valued state space where the statistics of the set-value state vector and set-valued measurement vector are approximated using their first-order moments (also referred to herein as an intensity) and applied in a Bayesian filter framework to estimate the IA track statistics using Gaussian mixtures. The resulting filter is referred to as a probabilistic hypothesis density (PHD) filter. An intensity refers to a weight, a state mean vector, and a state covariance matrix of an element of the set-value state space where this element corresponds to the statistics of a track.
A basic PHD filter typically clusters new, current, and measurement-to-track intensities by comparing the Malhanobis distance between the statistics of two intensities. If this distance is below a user selected threshold, then the two intensities are considered to have originated from the same IA. This process involves comparing all possible intensity pairs to the threshold.